A Multi-Omics Approach to Examine Symptoms and Medication Adherence in Women with Breast Cancer Breast cancer is most prevalent in postmenopausal women, and 3.4 million U.S. women are survivors. Most women with breast cancer are postmenopausal at the time of diagnosis, and at least 70% of tumors are hormone receptor positive (HR+). Aromatase inhibitors effectively prevent BC recurrence, and current standard includes adjuvant aromatase inhibitor (AI) therapy in a once daily standard dose regimen for a minimum of five years. However, AI adherence is a significant problem. Up to one third of women do not fill their initial AI prescription, adherence to AIs averaged 48% in the first year, and adherence decreases in subsequent 2-5 years. AI-attributed symptoms are the leading reason for not adhering to AI regimens and a major barrier to AI adherence. Moreover, AI-related symptom type and severity are highly variable among women. The source of symptom variability is unknown. The etiology of symptoms experienced during AI therapy may have biological underpinnings, yet little is known about factors in AI (anastrozole, letrozole, exemestane) absorption, distribution, metabolism, and elimination (ADME) pathways and the resulting symptoms. Symptoms and adherence, especially their relationship to each other, have not been well-characterized temporally. Further, potential biologic mechanisms related to AI absorption, distribution, metabolism, and elimination (ADME) for AIs have not been fully characterized. The dissertation project (F99) will examine temporal patterns of AI symptoms and adherence and their relationship over the first 18 months of AI therapy. It will also explore the role of genotypic (ADME) and phenotypic factors in symptoms experienced and AI adherence. The postdoctoral project (K00 phase) will incorporate two additional molecular methods?microbiomics and exosomics. They will be described and their potential role in AI symptoms experienced and adherence will be explored. The purpose of the proposed F99/K00 training and research is to utilize a biobehavioral, multi-omics approach to gain a deeper understanding of the complex web of AI symptoms and adherence, including the temporal variability among women and the interplay between symptoms and adherence. It will provide insight into potential biological mechanisms by describing molecular and phenotypic characteristics associated with symptoms and adherence. Ultimately, this research will inform future symptom management and adherence interventions and their timing.